HoPPF: A novel local surface descriptor for 3D object recognition

Abstract

Three-dimensional feature descriptors play an important role in 3D computer vision because they are widely employed in many 3D perception applications to extract point correspondences between two point clouds. However, most existing description methods suffer from either weak robustness, low descriptiveness, or costly computation. Thus, a 3D local feature descriptor named Histograms of Point Pair Features (HoPPF) is proposed in this paper, and it is aimed at robust representation, high descriptiveness, and efficient computation. First, we propose a novel method to redirect surface normals and use the Poisson-disk sampling strategy to solve the problem of data redundancy in data pre-processing. Second, a new technique is applied to divide the local point pair set of each keypoint into eight regions. Then, the distribution of local point pairs of each region is used to construct the corresponding sub-features. Finally, the proposed HoPPF is generated by concatenating all sub-features into a vector. The performance of the HoPPF method is rigorously evaluated on several standard datasets. The results of the experiments and comparisons with other state-of-the-art methods validate the superiority of the HoPPF descriptor in term of robustness, descriptiveness, and efficiency. Moreover, the proposed technique for division of point pair sets is used to modify the other typical point-pair-based descriptor (i.e., PFH) to show its generalization ability. The proposed HoPPF is also applied to object recognition on real datasets captured by different devices (e.g., Kinect and LiDAR) to verify the feasibility of this method for 3D vision applications.